Machine-Learning Based Prediction of Kinetic Rate Coefficients in Radical Polymerization

04 November 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Using a log-log regression, the propagation rate coefficients for radical polymerization are correlated with basic molecular properties. These are either available from literature, or from simple and non-time-consuming calculations. Parameters under consideration are molecular weights, boiling points or dipolar moments. The model brings acrylates and methacrylates with linear and branched structures, and monomers that are known to be influenced strongly by H-bonding in line with each other, allowing to fit all data in a single approach. The model also successfully correlates monomers such as styrene and acrylonitrile successfully. Both absolute rate coefficients, as well as Arrhenius activation parameters can be described with high accuracy. With the presented model it is thus possible to describe practically all monomers for which kinetic data is available simultaneously and to carry out predictions for monomers for which no experimental data exist.


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